--[[
- Written by Francois Fleuret (francois@fleuret.org)
-
- This is free and unencumbered software released into the public
- domain.
-
- Anyone is free to copy, modify, publish, use, compile, sell, or
- distribute this software, either in source code form or as a
- compiled binary, for any purpose, commercial or non-commercial, and
- by any means.
-
- In jurisdictions that recognize copyright laws, the author or
- authors of this software dedicate any and all copyright interest in
- the software to the public domain. We make this dedication for the
- benefit of the public at large and to the detriment of our heirs
- and successors. We intend this dedication to be an overt act of
- relinquishment in perpetuity of all present and future rights to
- this software under copyright law.
-
- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
- NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY
- CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
- For more information, please refer to <http://unlicense.org/>
+ Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/
+ Written by Francois Fleuret <francois.fleuret@idiap.ch>
+
+ This file is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License version 3 as
+ published by the Free Software Foundation.
+
+ It is distributed in the hope that it will be useful, but WITHOUT
+ ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
+ or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
+ License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this file. If not, see <http://www.gnu.org/licenses/>.
]]--
-- Create a model
+local w, h, fs = 50, 50, 3
+local nhu = (w - fs + 1) * (h - fs + 1)
+
local model = nn.Sequential()
:add(nn.Sequential()
- :add(nn.Linear(1000, 1000))
+ :add(nn.SpatialConvolution(1, 1, fs, fs))
+ :add(nn.Reshape(nhu))
+ :add(nn.Linear(nhu, 1000))
:add(nn.ReLU())
)
:add(nn.Linear(1000, 100))
--- Decor it for profiling
+-- Decorate it for profiling
-profiler.decor(model)
-print()
+profiler.decorate(model)
-- Create the data and criterion
-local input = torch.Tensor(1000, 1000)
+local input = torch.Tensor(1000, 1, h, w)
local target = torch.Tensor(input:size(1), 100)
local criterion = nn.MSECriterion()
-- Print the accumulated timings
+print()
+-- profiler.color = false
profiler.print(model, nbSamples)
-- profiler.print(model)
-print()
print(string.format('Total model time %.02fs', modelTime))
print(string.format('Total data time %.02fs', dataTime))